Talk:Mann-Whitney U
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[edit] Assumptions
The article gives a misleading information on the assumptions of Mann-Whitney U test. It says:
"In a less general formulation, the Wilcoxon-Mann-Whitney two-sample test may be thought of as testing the null hypothesis that the probability of an observation from one population exceeding an observation from the second population is 0.5. This formulation requires the additional assumption that the distributions of the two populations are identical except for possibly a shift (i.e. f1(x) = f2(x + δ) )"
Testing the alternative hypothesis P(A>B) > 0.5 (where A is from population 1 and B is from pupulation 2) does not require the restricting assumption that both distributions are equal except for a shift in location! How come? What is the basis for this statement? The test statistic in U-test is just the proportion of pairs such that the first observation is from population 1 and the second from population 2. The distribution of this test statistic can be perhaps most easily be theoretically calculated for the special case shifted distributions but it does not restrict the use of the test and has nothing to do with test assumptions! —Preceding unsigned comment added by Marenty (talk • contribs) 22:06, 24 May 2008 (UTC)
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can anyone typeset the formulae better? I am not familiar with Tex. seglea 05:39, 17 Jan 2004 (UTC)
[edit] Assumptions
The hypothesis stated in this article refers both to the testing of equality of central tendency, and equality of distribution. The central tendancy hypothesis requires the additional assumption that the distribution of the two samples are the same except for a shift (i.e. f1(X) = f2(X+delta)). The test can also be described as a general test of equality of distribution (H0: f1=f2). In this case the shift alternative is not required, however, the test is used most often as a test of central tendency, so the original formulation (with the addition of the shift assumption) is most appropriate. I have added this assumption to the main page. —Preceding unsigned comment added by 132.239.102.171 (talk) 00:20, 30 January 2008 (UTC)
[edit] Ambiguity in the direct method
The second point of the direct method should better be: "Taking each observation in sample 1, count the number of observations in sample 2 that are GREATER than it." In the example with the tortoises, we see that for each tortoise, we count hares with a greater rank (or time) in the race, not smaller. Am I wrong? --Jgiard 10:07, 5 December 2006 (UTC)
[edit] P value
I beleive that one cannot interpret results from this test with out understanding the P-value. As I understand it the smaller the P value the more different the two populations are. What I would like to know if there is a critical value like there is with a T-test? Thanks ADS
[edit] Inexact explanation of what this test should be used for
A significant MW tests does not necessarily imply that the distributions have different medians. This is a common misconception. It is most powerful for detecting a difference in medians, which is why this is commonly misstated. The MW tests that the samples were taken from different distributions.
[edit] calculation of mu in the normal approx
Is this right? I would have thought it should be symmetrical in n1 and n2...
161.130.68.84 21:11, 28 December 2006 (UTC)JWD
[edit] Introduction requires clarity
These two statements are not equivelent
- It requires the two samples to be independent, and the observations to be ordinal or continuous measurements
- i.e. one can at least say, of any two observations, which is the greater.
[edit] Link to rank article needs to be more specific
The 'rank' link currently points to a disambiguation page which doesn't include an article explaining what the rank of a sample is.Tim (talk) 02:31, 3 June 2008 (UTC)
